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In case your model is a custom PyTorch model, one can leverage the PyTorchModelHubMixin class as it allows to add from_pretrained, push_to_hub to any nn.Module class, just like models in the Transformers, Diffusers and Timm libraries.
- Share a model
After you fine-tune your model, call push_to_hub() on...
- Model sharing and uploading
Directly push your model to the hub. Once you have an API...
- Share a model
After you fine-tune your model, call push_to_hub() on Trainer to push the trained model to the Hub. 🤗 Transformers will even automatically add training hyperparameters, training results and framework versions to your model card!
Directly push your model to the hub. Once you have an API token (either stored in the cache or copied and pasted in your notebook), you can directly push a finetuned model you saved in save_directory by calling: finetuned_model.push_to_hub("my-awesome-model")
Aug 9, 2024 · This article provides a comprehensive guide on how to upload and share a model on Hugging Face, covering the necessary steps, best practices, and tips for optimizing your model’s visibility and usability.
Check out https://Trelis.com for ADVANCED fine-tuning resources!Video Resources:- Notebook: https://github.com/TrelisResearch/install-guides/blob/main/Pushin...
- 45 min
- 2.4K
- Trelis Research
Aug 19, 2023 · 1. I believe you can just use model.push_to_hub() after authenticating. See the page here: https://huggingface.co/docs/transformers/model_sharing. Authenticate: # via bash. huggingface-cli login. # via python & Jupyter. pip install huggingface_hub. from huggingface_hub import notebook_login.
Jul 19, 2022 · You can save models with trainer.save_model("path_to_save"). Another cool thing you can do is you can push your model to the Hugging Face Hub as well. I added couple of lines to notebook to show you, here. You can find pushing there.
